Planning for Urban Satisfaction
–A critical approach to measure the quality of urban experience from a social media platform
Yuanzhao WANG
Supervisor: Carole Voulgaris
Abstract
This paper aims to examine the strengths and weaknesses of social media platforms and proposes a vision for a better platform to invite bottom-up citizen participation in data-driven urban planning for urban satisfaction. The research proposes a method to measure the emotional response of individual citizens to the characteristics of the built environment focusing on proximity and convenience between pedestrians and nearby commercial and cultural activities. The goal is to explore the relationships between citizens’ sentiments, urban form, and urban activities, and build a regression model for optimizing urban satisfaction. By examining the existing Weibo data, the initial research revealed serious limitations that existing social media data that would merely provide sufficient information for quantitative urban planning. Finally, it proposes a better platform that can fulfill the need for more accurate data for quantifying individual urban satisfaction.
Introduction
In China, the early stage of urbanization was subject to the national ambition and development, whose aim at boosting the economic production and consumption. As such, hundreds and thousands of cities and towns were built to serve for this purpose that plays an important role in improving the national economy based on the scarification of natural resource and incline of factory production (Zhang et al., 2020). However, since the decreasing demand for industrial production and the arise of urban awareness of residents happiness, the government has issued the “New Urbanization (citization and townization) Policy” and “Specialty Townization Policy” since 2014, increasingly focus on citizen wellbeing, urban governance and the urban environment (State Council of China, 2014).
The new urban policies in China shifted the emphasis of urbanization away from economic development and toward human-centric development, improving residents’ wellbeing and building new ecological smart cities. However, the evaluation of residents’ wellbeing and urban planning process remains top-down and entirely conducted by governments and experts (figure 1). The qualitative surveys and reports could only cover a small proportion of population, and it becomes even harder and time-consuming as the population in cities and towns reached 848 millions (State Council of China, 2010).
figure 1 Top-down urban planning in China
With the introduction of social media data and machine learning technologies, new bottom-up data and methods for studying urban spatial patterns and residents’ life have emerged. The ideal bottom-up data for planner should have three characteristics. First, easy Participation: People should be easy to participate, which means people don’t need to leave a period of time and show up in a specific place to engage. Second, public Access: most people should be able to access. Lastly, people should be able to express their long-tome expectations of the city and their satisfaction regarding the existing conditions.
In 2022, there were more than 1.02 billion people have access to mobile internet, while 68% of them frequently use social media platform (DataReportal, 2022). The large coverage of population and easy participation of social media data can fulfill certain needs of ideal data but not enough.The existing data can only generate real-time daily experience but not specific for the built environment or for long-term wellbeing (figure 2).
To address the shortcomings of existing data and break the traditional top-down approach to urban planning, a thorough validation and a quantitative framework is required. I propose a research framework measuring the emotional response of individual citizens to the characteristics of the built environment focusing on proximity and convenience between pedestrians and nearby commercial and cultural activities. The goal is to explore the relationships between citizens’ satisfaction, urban form, and urban activities. This approach can be applied to bring public satisfaction into the urban planning process through the social platform as a way to quantify the quality of urban life (figure 2). However, the simulation model based on the existing Weibo data revealed serious limitations. The discussion of the strengths and drawbacks of existing social media data leads to a vision for a better platform to invite bottom-up citizen participation in data-driven urban planning for urban satisfaction.
Literature Review
Wellbeing as a Measure of Health and the Built environment
Scientists have historically measured well-being using objective indicators (e.g., GDP, health, employment, literacy, poverty) and increasing measured subjective well-being that influences individual life. Modern measures of well-being that account for cognitive evaluations (i.e., evaluative well-being) and reactions to experiences (i.e., experienced well-being) have therefore become the “currency of a life that matters” (Rath et al., 2010). As the concept of well-being develops, the indices including physical health, mental health, air quality and more are increasingly used, implying a strong relationship between health and residents’ well-being (Diener et al., 1999; Lawless & Lucas, 2010). Mouratidis (2018) further investigated different aspects of subjective well-being (SWB): hedonic, life satisfaction, and eudaimonic. He categorized neighborhood characteristics into objective and perceived, and proposed a conceptual framework to explain how neighborhood characteristics might affect SWB by inviting a mediating factors such as personal relationships, leisure activities, health, and neighborhood impact on emotions and mood.There are increasing numbers of studies investigating how the built environment may affect individual wellbeing. Some studies found that population density may affect well-being on the city level (Florida et al., 2013). Social and human capital, considered significant drivers of urban well-being, can be affected by safety, educational opportunities, and access to arts and culture (Leyden et al., 2011; Florida et al., 2013). Other aspects of urban infrastructure (such as roads and transportation) impact commute time and connectedness, both of which are related to happiness (Yin et al., 2021; Gim, 2021).
Quantitative urban measure of the built environment
The measurement of the built environment is constructed by a variety body of indices to address different urban issues. Cervero and Kockelman’s developed initial “three Ds” (density, design, and diversity) in 1997 to evaluate the existing urban built environment. Edwing et al. expanded on this concept by adding two Ds (destination accessibility and transportation distance) (Ewing & Cervero, 2001; Ewing et al., 2009). More Ds were added afterwards to reflect the changing built environment, such as Demand management and Demographics (Ewing & Cervero, 2010). Scholars have modified the list of variables based on these quantitative frameworks to comprehensively examine the built environment while addressing various urban issues and topics. Some research used relative entropy to discern compactness from sprawl in the built environment (Tsai, 2005). Others used a multi-metric urban intensity index at a metropolitan scale, which included land use, infrastructure, and landscape variables in addition to density and compactness (Tate et al., 2005). More recent studies, especially in the Chinese context, Rowe et al. (2014) proposed the measurement of urban intensity from variables of compactness, density, diversity, and connectivity, aiming at revealing the resource distribution, transportation efficiency, and social integration in both cities and optimize the urban performance (Rowe et al., 2014). Later, Guan and Rowe (2016) evaluated the spatial structure of small towns in Zhejiang Province using similar urban intensity characteristics, such as density, compactness, diversity, and accessibility.
Limitation
clearly define the research question(s):
What data is needed for data-driven land-use planning to optimize well-being? Can social media data fill those data needs? What data-gathering tool might fill those needs better than social media?
- First, although the relationships between public sentiment and the proximity to amenities are indicated by a set of regression models, there are no proof to suggest any causation;
- Second, in this research, author assumed the public sentiment from social media could represent individual real-time happiness, which remains uncertain awaiting a further examination;
- Third, due to the limited urban variables, the confidence level of regression model is relatively low at 2%, which could be used to study the changes of individual sentiment but is incapable to simulate a convincing results;
- Forth, the inherent conflict between short-term resident’s feeling from existing social media data and long-term urban planning process is recognized;
- Fifth, the existing social media data is not specific for the built environment or urban experience but daily expression of everyday life;
- Sixth, the weibo post could only represent the users who mostly age from 16-40, which are around 30% of the total population who have access to internet.
Methodology
The research aims to apply sentiment analysis to quantify qualitative public sentiment from text-based information as a representation of individual real-time feeling, and develop a multivariate model to explore the relationship between the individual feelings from social media and the built environment in China. Based on the model, this research compare existing condition and the planned condition of Jiaxing city in China, and test different planning scenarios to explore the possible changes of individual feeling when using social media platform.
Site
The study area is Jiaxing city in China’s Zhejiang province. Jiaxing is a significant city that is part of the Yangtze River Delta city cluster and the Shanghai metropolitan area. It is located in close proximity to the two major cities of Shanghai and Hangzhou. It is a small tourist city with two counties, 44 towns, and 809 administrative villages that has been designated as a national key program.
Data collection and preprocess
For this research, social media data (Weibo posts) was collected from Sina Weibo, which is one of the most used online social platform in China. The collected Weibo posts are restricted with the tag “Jiaxing” in 2018, which limited the posts related to the case city. The untreated data included a large amount of advertisements, which have been removed by identifying certain keywords such as “advertisement”, “cost performance”, etc. The urban amenity data is collected from Gaode Map as spatial POI (points of interest). The regulatory detailed planning (RDP) documents from 2017-2020 issued by the local government in Jiaxing are collected from the local government office.
Proximity to urban amenities
In this research, proximity is defined by the accessibility of urban activities and amenities by walking and by characteristics of pedestrian networks. The amenities dataset is collected as points of interest (POIs) from Gaode Map in China, which is one of the most widely used digital navigation systems in China. The urban networks are extracted from the OpenStreet Map (OSM). Accessibility will be calculated by the R5 package embedded in the R studio.